arXiv subjects

Kun Yang

Publications and source records attributed to Kun Yang.

At least 109 records · Page 6Linked to original sources

arXiv2025

Probing Temperature at Nanoscale through Thermal Vibration Characterization using Scanning Precession Electron Diffraction

Accurate, non-contact temperature measurement with high spatial resolution is essential for understanding thermal behavior in integrated nanoscale devices and heterogeneous interfaces. However, existing techniques are often limited by the need for physical contact or insufficient spatial resolution for the measurement of local temperature and mapping its distribution. Here, we showcase the direct temperature measurement of graphene with nanometer spatial resolution in transmission electron microscopy. In experiments, combining a scanning nanobeam with precession electron diffraction offers the collection of kinemetic diffraction from a local area at the nanometer scale. In analysis, we use a pre-calculated, sample-specific structure-factor-based correction method to enable the linear fitting of the diffraction intensities, allowing the determination of the Debye-Waller factor as a function of temperature at the precision of 10-4Å2/°C. With the high spatial resolution and measurement precision, the temperature and thermal vibration mapping further reveal the influence of graphene lattice parameters and thickness on the Debye-Waller factor, providing valuable insights into the vibrational properties impacted by temperature, lattice structure, and graphene layer thickness.

cond-mat.mtrl-sci↗

arXiv2025

Fermion liquids as quantum Hall liquids in phase space: A unified approach for anomalies and responses

The discovery of many strongly correlated metallic phases has inspired different routes to generalize or go beyond the celebrated Landau Fermi liquid theory. To this end, from universal consideration of symmetries and anomalies, Else, Thorngren and Senthil (ETS) have introduced a class of theories called ersatz Fermi liquids which possess a Fermi surface and satisfy a generalized Luttinger's theorem. In this work, we view all such fermion liquids obeying the Luttinger theorem as incompressible quantum Hall liquids in higher-dimensional phase space and use it as the starting point to derive their effective low-energy field theory. The noncommutativity of phase space motivates us to use the Seiberg-Witten map to derive the field theory in an ordinary (commutative) space and naturally leads to terms that correspond to the correct topological Chern-Simons action postulated by ETS in one, two, and three dimensions. Additionally, our approach also reproduces all the non-topological terms that characterize important contributions to the response, including the semiclassical equations of motion. Finally, our derivations of Chern-Simons terms from the Seiberg-Witten map also verify a longstanding conjecture in noncommutative field theory.

cond-mat.str-el↗

arXiv2025

Precise Interception Flight Targets by Image-based Visual Servoing of Multicopter

Vision-based interception using multicopters equipped strapdown camera is challenging due to camera-motion coupling and evasive targets. This paper proposes a method integrating Image-Based Visual Servoing (IBVS) with proportional navigation guidance (PNG), reducing the multicopter's overload in the final interception phase. It combines smoother trajectories from the IBVS controller with high-frequency target 2D position estimation via a delayed Kalman filter (DKF) to minimize the impact of image processing delays on accuracy. In addition, a field-of-view (FOV) holding controller is designed for stability of the visual servo system. Experimental results show a circular error probability (CEP) of 0.089 m (72.8% lower than the latest relevant IBVS work) in simulations and over 80\% interception success under wind conditions below 4 m/s in real world. These results demonstrate the system's potential for precise low-altitude interception of non-cooperative targets.

cs.RO↗

arXiv2025

Photostriction Facilitates Relaxation of Lattice Distortion in Two-Dimensional Perovskites

The photostriction effect, a light-induced mechanical deformation in materials, originates from the intricate interplay between lattice structure and electronic excitation. In photovoltaic semiconductors, this effect plays a crucial role in shaping non-equilibrium structural responses, yet its fundamental mechanism remains elusive. Here, we uncover lattice expansion and structural reconfiguration in two-dimensional (2D) perovskites driven by photoinduced excitation using first-principles calculations. Our findings reveal that the photoinduced carriers lead to a substantial lattice expansion by about 2%. The expanded lattice facilitates strain relaxation with the amplitude of 20% by increasing interatomic distances and reducing internal stresses, thereby enhancing structural stability. The lattice dynamics can be systematically engineered through photodoping density, unveiling a new pathway to modulate light-matter interactions in 2D perovskites. These insights not only advance the understanding of optically driven structural dynamics but also offer a guiding principle for optimizing next-generation high-efficiency photovoltaic devices and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2025

Robust Full-Space Physical Layer Security for STAR-RIS-Aided Wireless Networks: Eavesdropper with Uncertain Location and Channel

A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty on the eavesdropper's position within the 360$^\circ$ service area offered by the STAR-RIS. Specifically, the large system analytical method is utilized to derive the asymptotic expression of the average security rate achieved by the security user, considering that the base station (BS) only has the statistical information of the eavesdropper's channel state information (CSI) and the uncertainty of its location. To evaluate the effectiveness of the proposed PLS scheme, we first formulate an optimization problem aimed at maximizing the weighted sum rate of the security user and the public user. This optimization is conducted under the power allocation constraint, and some practical limitations for STAR-RIS implementation, through jointly designing the active and passive beamforming variables. A novel iterative algorithm based on the minimum mean-square error (MMSE) and cross-entropy optimization (CEO) methods is proposed to effectively address the established non-convex optimization problem with discrete variables. Simulation results indicate that the proposed robust PLS scheme can effectively mitigate the information leakage across the entire coverage area of the STAR-RIS-assisted system, leading to superior performance gain when compared to benchmark schemes encompassing traditional RIS-aided scheme.

cs.IT↗

arXiv2025

Reconfigurable Intelligent Sensing Surface enables Wireless Powered Communication Networks: Interference Suppression and Massive Wireless Energy Transfer

Recently, a novel structures of reconfigurable intelligent surface (RIS) integrating both passive and active elements, termed reconfigurable intelligent sensing surface (RISS), efficiently addresses challenges in RIS channel estimation and mitigates issues related to multiplicative path loss by processing the signal at the RISS. In this paper, we propose a sensing-assisted wirelessly powered communication network (WPCN) that utilizes RISS's sensing capabilities to maximize the channel capacity in uplink wireless information transfer (WIT) and assist in massive wireless energy transmission (WET) for downlink. For the WIT in the uplink, the sensing information is utilized to design an interference suppression passive reflection phase shift for the RISS, and take the imperfect sensing results and sharp null into consideration, we also propose a robust scheme. For the WET in the downlink, the massive WET scheme is adopted and benefits from a period of sensing results. The massive WET scheme including beam selection and rotation order optimization to enhance the lower bound of energy harvest for massive users and optimize waiting costs. Numerical results demonstrate the optimal interference suppression threshold for uplink WIT and underscore the achieved fairness in downlink WET. Collectively, by utilizing sensing information, the uplink channel capacity is improved by 20\%, and the worst energy performance and waiting costs for massive WET are effectively optimized, with improvements ranging from 19\% to 59\% and 27\% to 29\%, respectively.

eess.SP↗

arXiv2025

Low-Complexity Beamforming Design for Null Space-based Simultaneous Wireless Information and Power Transfer Systems

Simultaneous wireless information and power transfer (SWIPT) is a promising technology for the upcoming sixth-generation (6G) communication networks, enabling internet of things (IoT) devices and sensors to extend their operational lifetimes. In this paper, we propose a SWIPT scheme by projecting the interference signals from both intra-wireless information transfer (WIT) and inter-wireless energy transfer (WET) into the null space, simplifying the system into a point-to-point WIT and WET problem. Upon further analysis, we confirm that dedicated energy beamforming is unnecessary. In addition, we develop a low-complexity algorithm to solve the problem efficiently, further reducing computational overhead. Numerical results validate our analysis, showing that the computational complexity is reduced by 97.5\% and 99.96\% for the cases of $K^I = K^E = 2$, $M = 4$ and $K^I = K^E = 16$, $M = 64$, respectively.

eess.SP↗

arXiv2025

Bedrock Models in Communication and Sensing: Advancing Generalization, Transferability, and Performance

Deep learning (DL) has emerged as a powerful tool for addressing the intricate challenges inherent in communication and sensing systems, significantly enhancing the intelligence of future sixth-generation (6G) networks. A substantial body of research has highlighted the promise of DL-based techniques in these domains. However, in addition to improving accuracy, new challenges must be addressed regarding the generalization and transferability of DL-based systems. To tackle these issues, this paper introduces a series of mathematically grounded and modularized models, referred to as bedrock models, specifically designed for integration into both communication and sensing systems. Due to their modular architecture, these models can be seamlessly incorporated into existing communication and sensing frameworks. For communication systems, the proposed models demonstrate substantial performance improvements while also exhibit strong transferability, enabling direct parameter sharing across different tasks, which greatly facilitates practical deployment. In sensing applications, the integration of the bedrock models into existing systems results in superior performance, reducing delay and Doppler estimation errors by an order of magnitude compared to traditional methods. Additionally, a pre-equalization strategy based on the bedrock models is proposed for the transmitter. By leveraging sensing information, the transmitted communication signal is dynamically adjusted without altering the communication model pre-trained in AWGN channels. This adaptation enables the system to effectively cope with doubly dispersive channels, restoring the received signal to an AWGN-like condition and achieving near-optimal performance. Simulation results substantiate the effectiveness and transferability of the proposed bedrock models, underscoring their potential to advance both communication and sensing systems.

eess.SP↗

arXiv2025

SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users' diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing decoding and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse sensing services and higher accuracy.

cs.LG↗

arXiv2025

Holographic Integrated Data and Energy Transfer

Thanks to the application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain by enabling the ability to generate any current distribution on the surface. With the aid of electromagnetic (EM) manipulation capability of H-MIMO, integrated data and energy transfer (IDET) system can fully exploits the EM channel to realize energy focusing and eliminate inter-user interference, which yields the concept of holographic IDET (H-IDET). In this paper, we invetigate the beamforming designs for H-IDET systems, where the sum-rate of data users (DUs) are maximized by guaranteeing the energy harvesting requirements of energy users (EUs). In order to solve the non-convex functional programming, a block coordinate descent (BCD) based scheme is proposed, wherein the Fourier transform and the equivalence between the signal-to-interference-plus-noise ratio (SINR) and the mean-square error (MSE) are also conceived, followed by the successive convex approximation (SCA) and an initialization scheme to enhance robustness. Numerical results illustrate that our proposed H-IDET scheme outperforms benchmark schemes, especially the one adopting traditional discrete antennas. Besides, the near-field focusing using EM channel model achieves better performance compared to that using the traditional channel model, especially for WPT where the EUs are usually close to the transmitter.

cs.IT↗

arXiv2025

Semantic Communications with Computer Vision Sensing for Edge Video Transmission

Despite the widespread adoption of vision sensors in edge applications, such as surveillance, the transmission of video data consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, preserving the accuracy and relevance of transmitted data while significantly reducing the volume of transmitted information. However, traditional SC methods face inefficiencies due to the repeated transmission of static frames in edge videos, exacerbated by the absence of sensing capabilities, which results in spectrum inefficiency. To address this challenge, we propose a SC with computer vision sensing (SCCVS) framework for edge video transmission. The framework first introduces a compression ratio (CR) adaptive SC (CRSC) model, capable of adjusting CR based on whether the frames are static or dynamic, effectively conserving spectrum resources. Additionally, we implement an object detection and semantic segmentation models-enabled sensing (OSMS) scheme, which intelligently senses the changes in the scene and assesses the significance of each frame through in-context analysis. Hence, The OSMS scheme provides CR prompts to the CRSC model based on real-time sensing results. Moreover, both CRSC and OSMS are designed as lightweight models, ensuring compatibility with resource-constrained sensors commonly used in practical edge applications. Experimental simulations validate the effectiveness of the proposed SCCVS framework, demonstrating its ability to enhance transmission efficiency without sacrificing critical semantic information.

cs.CV↗

arXiv2025

NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on Thermodynamics

Thermal infrared imaging offers the advantage of all-weather capability, enabling non-intrusive measurement of an object's surface temperature. Consequently, thermal infrared images are employed to reconstruct 3D models that accurately reflect the temperature distribution of a scene, aiding in applications such as building monitoring and energy management. However, existing approaches predominantly focus on static 3D reconstruction for a single time period, overlooking the impact of environmental factors on thermal radiation and failing to predict or analyze temperature variations over time. To address these challenges, we propose the NTR-Gaussian method, which treats temperature as a form of thermal radiation, incorporating elements like convective heat transfer and radiative heat dissipation. Our approach utilizes neural networks to predict thermodynamic parameters such as emissivity, convective heat transfer coefficient, and heat capacity. By integrating these predictions, we can accurately forecast thermal temperatures at various times throughout a nighttime scene. Furthermore, we introduce a dynamic dataset specifically for nighttime thermal imagery. Extensive experiments and evaluations demonstrate that NTR-Gaussian significantly outperforms comparison methods in thermal reconstruction, achieving a predicted temperature error within 1 degree Celsius.

cs.CV↗

arXiv2025

Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving

Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sensitivity of online tasks, over-provisioning resources is common practice. This allows for the integration of latency-insensitive offline tasks during periods of low online load, enhancing resource utilization. However, strategically serving online and offline tasks through a preemption mechanism fails to fully leverage the flexibility of offline tasks and suffers from KV cache recomputation and irregular workloads. In this paper, we introduce Echo, a collaborative online-offline task serving system, including a scheduler, a KV cache manager, and estimation toolkits. The scheduler and KV cache manager work tightly to maximize the throughput of offline tasks, while the estimator further predicts execution time to ensure online task SLOs. The scheduler leverages the batch information of last iteration to reduce the search space for finding the optimal schedule. The KV cache manager sets the priority of the KV cache based on the type of tasks and the opportunity of prefix sharing to reduce the recomputation. Finally, the estimation toolkits predict the execution time, future memory consumption, and the throughput of offline tasks to guide the scheduler, KV cache manager, and the system deployer. Evaluation based on real-world workloads demonstrates that Echo can increase offline task throughput by up to $3.3\times$, while satisfying online task SLOs.

cs.DC↗

arXiv2025

CommGPT: A Graph and Retrieval-Augmented Multimodal Communication Foundation Model

Large Language Models (LLMs) possess human-level cognitive and decision-making capabilities, making them a key technology for 6G. However, applying LLMs to the communication domain faces three major challenges: 1) Inadequate communication data; 2) Restricted input modalities; and 3) Difficulty in knowledge retrieval. To overcome these issues, we propose CommGPT, a multimodal foundation model designed specifically for communications. First, we create high-quality pretraining and fine-tuning datasets tailored in communication, enabling the LLM to engage in further pretraining and fine-tuning with communication concepts and knowledge. Then, we design a multimodal encoder to understand and process information from various input modalities. Next, we construct a Graph and Retrieval-Augmented Generation (GRG) framework, efficiently coupling Knowledge Graph (KG) with Retrieval-Augmented Generation (RAG) for multi-scale learning. Finally, we demonstrate the feasibility and effectiveness of the CommGPT through experimental validation.

cs.IT↗

arXiv2025

Photoexcitation-induced Stacking Transition Assisted by Intralayer Reconstruction in Charge-Density-Wave Materials

Laser excitation has emerged as an effective tool for probing microscopic interactions and manipulating phases of matter. Among charge density wave (CDW) materials, 1T-TaS2 has garnered significant attention due to its diverse stacking orders and photoexcited responses. However, the mechanisms driving transitions among different stacking orders and the microscopic out-of-equilibrium dynamics remain unclear. We elucidate that photoexcitation can introduce interlayer stacking order transitions facilitated by laser-induced intralayer reconstruction in 1T-TaS2. Importantly, our finding reveals a novel pathway to introduce different phases through laser excitations, apparently distinct from thermally-induced phase transitions via interlayer sliding. In particular, photoexcitation is able to considerably change potential energy surfaces and evoke collective lattice dynamics. Consequently, the laser-induced intralayer reconstruction plays a crucial role in interlayer stacking-order transition, offering a new method to create exotic stackings and quantum phases. The exploration opens up great opportunities for manipulating CDW phases and electronic properties on the femtosecond timescale.

cond-mat.mtrl-sci↗

arXiv2025

M4SC: An MLLM-based Multi-modal, Multi-task and Multi-user Semantic Communication System

Multi-modal Large Language Models (MLLMs) are capable of precisely extracting high-level semantic information from multi-modal data, enabling multi-task understanding and generation. This capability facilitates more efficient and intelligent data transmission in semantic communications. In this paper, we design a tailored MLLM for semantic communication and propose an MLLM-based Multi-modal, Multi-task and Multi-user Semantic Communication (M4SC) system. First, we utilize the Kolmogorov-Arnold Network (KAN) to achieve multi-modal alignment in MLLMs, thereby enhancing the accuracy of semantics representation in the semantic space across different modalities. Next, we introduce a multi-task fine-tuning approach based on task instruction following, which leverages a unified task instruction template to describe various semantic communication tasks, improving the MLLM's ability to follow instructions across multiple tasks. Additionally, by designing a semantic sharing mechanism, we transmit the public and private semantic information of multiple users separately, thus increasing the efficiency of semantic communication. Finally, we employ a joint KAN-LLM-channel coding strategy to comprehensively enhance the performance of the semantic communication system in complex communication environments. Experimental results validate the effectiveness and robustness of the proposed M4SC in multi-modal, multi-task, and multi-user scenarios.

cs.IT↗

arXiv2025

Average Reward Reinforcement Learning for Wireless Radio Resource Management

In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch between the discounted reward RL formulation and the undiscounted goal of wireless network optimization. To the best of our knowledge, we are the first to systematically investigate this discrepancy, starting with a discussion of the problem formulation followed by simulations that quantify the extent of the gap. To bridge this gap, we introduce the use of average reward RL, a method that aligns more closely with the long-term objectives of RRM. We propose a new method called the Average Reward Off policy Soft Actor Critic (ARO SAC) is an adaptation of the well known Soft Actor Critic algorithm in the average reward framework. This new method achieves significant performance improvement our simulation results demonstrate a 15% gain in the system performance over the traditional discounted reward RL approach, underscoring the potential of average reward RL in enhancing the efficiency and effectiveness of wireless network optimization.

cs.IT↗

arXiv2024

Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL.

cs.LG↗